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Avgis Hadjipapas - One of the best experts on this subject based on the ideXlab platform.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
NeuroImage, 2021Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for Gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to Gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning Gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local Gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
bioRxiv, 2020Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Abstract Here we present experimentally constrained computational models of Gamma Rhythm and use these to investigate Gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to Gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the model’s capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which Gamma oscillations contribute and could serve as a basis for future, more complex models.
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contrast dependent modulation of Gamma Rhythm in v1 a network model
BMC Neuroscience, 2015Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:In our empirical data comprising of single-unit and LFP recordings in macaque area V1 and source reconstructed human MEG localized to visual cortex we have observed a robust increase in Gamma oscillation frequency with increasing luminance contrast. In addition, at high grating contrasts, a robust decay in Gamma power was observed in the LFP [1] but not the MEG. These phenomena are key to understanding the functional role of network frequencies and for investigating the stability of Gamma oscillations at both local and macroscopic levels. However, even at the most basic level of spatially- undifferentiated neuronal models, it is not fully understood how excitatory (E) and inhibitory (I) neurons interact to generate the observed network Gamma oscillations in the macaque single-unit and LFP data. For example we could obtain the frequency shift and power decay in a network where the Rhythm is produced by excitatory neurons that fired more frequently than inhibitory neurons, and in another more neurophysiologically plausible network composed of excitatory neurons showing sparse firing [2,3] and inhibitory neurons showing faster firing [4]. Moreover, it is unknown how increasing excitatory afferent drive (of which luminance contrast is a proxy) modulates the interactions between E and I populations (as well as interactions within each population) to account for changes in frequency and power. We aimed to replicate the empirical data from macaque visual cortex and to further investigate the stability of the observed Gamma oscillation. Here, we present an undifferentiated V1 network PING model, with realistic neuronal features as determined and validated from the analysis of a large number of V1 neurons obtained in 3 rhesus monkeys. The model when perturbed by increasing afferent input, exhibits the core characteristics of the empirical data, that is, (1) a monotonic increase in LFP frequency, (2) a non-monotonic LFP power modulation with decay at high inputs, (3) a largely non-saturating increase in average unit firing rate. In addition, the model exhibits realistic single unit behavior across a range of inputs. In terms of the frequency shift, we have observed remarkable scaling behaviour: while the frequency of oscillations changes dramatically with input, the absolute average phase at which inhibitory and excitatory neurons fire in each oscillation cycle and the average relative phase to each other remain constant. This scaling may on one hand underlie the stability of the Gamma oscillation locally and on other hand facilitate communication through coherence in the Gamma range [5] across varying stimulus conditions, by preserving the timing and relative ordering of population firing irrespective of the oscillation frequency [6]. Our results suggest that the observed power decline results from a primary (functional) decoupling among inhibitory neurons. Further analysis highlighted that the functional decoupling is related to the balance of inhibition/excitation. In further steps, we intend to test these predictions in the empirical data, and then proceed to a differentiated V1 columnar model to investigate the divergences between human MEG and macaque LFP/spiking responses.
Margarita Zachariou - One of the best experts on this subject based on the ideXlab platform.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
NeuroImage, 2021Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for Gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to Gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning Gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local Gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
bioRxiv, 2020Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Abstract Here we present experimentally constrained computational models of Gamma Rhythm and use these to investigate Gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to Gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the model’s capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which Gamma oscillations contribute and could serve as a basis for future, more complex models.
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contrast dependent modulation of Gamma Rhythm in v1 a network model
BMC Neuroscience, 2015Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:In our empirical data comprising of single-unit and LFP recordings in macaque area V1 and source reconstructed human MEG localized to visual cortex we have observed a robust increase in Gamma oscillation frequency with increasing luminance contrast. In addition, at high grating contrasts, a robust decay in Gamma power was observed in the LFP [1] but not the MEG. These phenomena are key to understanding the functional role of network frequencies and for investigating the stability of Gamma oscillations at both local and macroscopic levels. However, even at the most basic level of spatially- undifferentiated neuronal models, it is not fully understood how excitatory (E) and inhibitory (I) neurons interact to generate the observed network Gamma oscillations in the macaque single-unit and LFP data. For example we could obtain the frequency shift and power decay in a network where the Rhythm is produced by excitatory neurons that fired more frequently than inhibitory neurons, and in another more neurophysiologically plausible network composed of excitatory neurons showing sparse firing [2,3] and inhibitory neurons showing faster firing [4]. Moreover, it is unknown how increasing excitatory afferent drive (of which luminance contrast is a proxy) modulates the interactions between E and I populations (as well as interactions within each population) to account for changes in frequency and power. We aimed to replicate the empirical data from macaque visual cortex and to further investigate the stability of the observed Gamma oscillation. Here, we present an undifferentiated V1 network PING model, with realistic neuronal features as determined and validated from the analysis of a large number of V1 neurons obtained in 3 rhesus monkeys. The model when perturbed by increasing afferent input, exhibits the core characteristics of the empirical data, that is, (1) a monotonic increase in LFP frequency, (2) a non-monotonic LFP power modulation with decay at high inputs, (3) a largely non-saturating increase in average unit firing rate. In addition, the model exhibits realistic single unit behavior across a range of inputs. In terms of the frequency shift, we have observed remarkable scaling behaviour: while the frequency of oscillations changes dramatically with input, the absolute average phase at which inhibitory and excitatory neurons fire in each oscillation cycle and the average relative phase to each other remain constant. This scaling may on one hand underlie the stability of the Gamma oscillation locally and on other hand facilitate communication through coherence in the Gamma range [5] across varying stimulus conditions, by preserving the timing and relative ordering of population firing irrespective of the oscillation frequency [6]. Our results suggest that the observed power decline results from a primary (functional) decoupling among inhibitory neurons. Further analysis highlighted that the functional decoupling is related to the balance of inhibition/excitation. In further steps, we intend to test these predictions in the empirical data, and then proceed to a differentiated V1 columnar model to investigate the divergences between human MEG and macaque LFP/spiking responses.
Mark O Cunningham - One of the best experts on this subject based on the ideXlab platform.
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rates and Rhythms a synergistic view of frequency and temporal coding in neuronal networks
Neuron, 2012Co-Authors: Matthew Ainsworth, Nancy Kopell, Mark O Cunningham, Roger D Traub, Shane Lee, Miles A. WhittingtonAbstract:In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs and demand for complex motor outputs how is the activity of such small subpopulations of neurons organized? Two theories dominate in this respect: increases in spike rate (rate coding) and sharpening of the coincidence of spiking in active neurons (temporal coding). Both have computational advantages and are far from mutually exclusive. Here, we review evidence for a bias in neuronal circuits toward temporal coding and the coexistence of rate and temporal coding during population Rhythm generation. The coincident expression of multiple types of Gamma Rhythm in sensory cortex suggests a mechanistic substrate for combining rate and temporal codes on the basis of stimulus strength.
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multiple origins of the cortical Gamma Rhythm
Developmental Neurobiology, 2011Co-Authors: Miles A. Whittington, Mark O Cunningham, Fiona E N Lebeau, Claudia Racca, Roger D TraubAbstract:Gamma Rhythms (30–80 Hz) are a near-ubiquitous feature of neuronal population activity in mammalian cortices. Their dynamic properties permit the synchronization of neuronal responses to sensory input within spatially distributed networks, transient formation of local neuronal “cell assemblies,” and coherent response patterns essential for intercortical regional communication. Each of these phenomena form part of a working hypothesis for cognitive function in cortex. All forms of physiological Gamma Rhythm are inhibition based, being characterized by Rhythmic trains of inhibitory postsynaptic potentials in populations of principal neurons. It is these repeating periods of relative enhancement and attenuation of the responsivity of major cell groups in cortex that provides a temporal structure shared across many millions of neurons. However, when considering the origins of these repeating trains of inhibitory events considerable divergence is seen depending on cortical region studied and mode of activation of Gamma Rhythm generating networks. Here, we review the evidence for involvement of multiple subtypes of interneuron and focus on different modes of activation of these cells. We conclude that most massively parallel brain regions have different mechanisms of Gamma Rhythm generation, that different mechanisms have distinct functional correlates, and that switching between different local modes of Gamma generation may be an effective way to direct cortical communication streams. Finally, we suggest that developmental disruption of the endophenotype for certain subsets of Gamma-generating interneuron may underlie cognitive deficit in psychiatric illness. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 92–106, 2011
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nmda receptor dependent switching between different Gamma Rhythm generating microcircuits in entorhinal cortex
Proceedings of the National Academy of Sciences of the United States of America, 2008Co-Authors: Steven J Middleton, Miles A. Whittington, Nancy Kopell, Fiona E N Lebeau, Jozsi Jalics, Tilman J Kispersky, Anita K Roopun, Mark O CunninghamAbstract:Local circuits in the medial entorhinal cortex (mEC) and hippocampus generate Gamma frequency population Rhythms independently. Temporal interaction between these areas at Gamma frequencies is implicated in memory—a phenomenon linked to activity of NMDA-subtype glutamate receptors. While blockade of NMDA receptors does not affect frequency of Gamma Rhythms in hippocampus, it exposes a second, lower frequency (25–35 Hz) Gamma Rhythm in mEC. In experiment and model, NMDA receptor-dependent mEC Gamma Rhythms were mediated by basket interneurons, but NMDA receptor-independent Gamma Rhythms were mediated by a novel interneuron subtype—the goblet cell. This cell was distinct from basket cells in morphology, intrinsic membrane properties and synaptic inputs. The two different Gamma frequencies matched the different intrinsic frequencies in hippocampal areas CA3 and CA1, suggesting that NMDA receptor activation may control the nature of temporal interactions between mEC and hippocampus, thus influencing the pathway for information transfer between the two regions.
Junliang Chen - One of the best experts on this subject based on the ideXlab platform.
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Gamma Rhythm oscillations and synchronization transition in a hybrid excitatory inhibitory complex network
World Congress on Services, 2020Co-Authors: Yuan Wang, Xia Shi, Bo Cheng, Junliang ChenAbstract:Spiking Neural Networks(SNN) stands out as a promising solution to perform complex computations or solve pattern recognition tasks, which is based on cerebral cortical dynamics of neuroscience. However, it is challenging for SNN to accurately capture the biological properties, since most SNN algorithms depend on the different variants of Integrate-and-Fire (IF) neuron model, which produces less biophysical properties of neural networks. Learning the mathematical foundations on how mammalian neocortex mechanism is performing in information processing and artificial intelligence is particularly important. This paper investigates the neural dynamics and Gamma oscillations in a complex network with balanced excitatory and inhibitory neurons (E-I network), as such networks are ubiquitous in the brain. The network consisting of hybrid regular spiking (RS) and chattering (CH) excitatory neurons and fast spiking (FS) inhibitory neurons emulated by the Izhikevich model, is designed to simulate the cortical regions of the human brain. Besides, the relationship between synchronization and Gamma Rhythm is explored by adjusting the critical parameters of our method. Experiments visually demonstrate that Gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the CH excitatory neurons can make the system easier to synchronize. These findings shed some light on further enhancements of the human brain and facilitate the development of artificial intelligence.
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neural dynamics and Gamma oscillation on a hybrid excitatory inhibitory complex network student abstract
National Conference on Artificial Intelligence, 2020Co-Authors: Yuan Wang, Xia Shi, Bo Cheng, Junliang ChenAbstract:This paper investigates the neural dynamics and Gamma oscillation on a complex network with excitatory and inhibitory neurons (E-I network), as such network is ubiquitous in the brain. The system consists of a small-world network of neurons, which are emulated by Izhikevich model. Moreover, mixed Regular Spiking (RS) and Chattering (CH) neurons are considered to imitate excitatory neurons, and Fast Spiking (FS) neurons are used to mimic inhibitory neurons. Besides, the relationship between synchronization and Gamma Rhythm is explored by adjusting the critical parameters of our model. Experiments visually demonstrate that the Gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the Chattering(CH) excitatory neurons can make the system easier to synchronize.
Mark Roberts - One of the best experts on this subject based on the ideXlab platform.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
NeuroImage, 2021Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for Gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to Gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning Gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local Gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.
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empirically constrained network models for contrast dependent modulation of Gamma Rhythm in v1
bioRxiv, 2020Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:Abstract Here we present experimentally constrained computational models of Gamma Rhythm and use these to investigate Gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including Gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to Gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the model’s capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which Gamma oscillations contribute and could serve as a basis for future, more complex models.
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contrast dependent modulation of Gamma Rhythm in v1 a network model
BMC Neuroscience, 2015Co-Authors: Margarita Zachariou, Mark Roberts, Eric Lowet, Peter De Weerd, Avgis HadjipapasAbstract:In our empirical data comprising of single-unit and LFP recordings in macaque area V1 and source reconstructed human MEG localized to visual cortex we have observed a robust increase in Gamma oscillation frequency with increasing luminance contrast. In addition, at high grating contrasts, a robust decay in Gamma power was observed in the LFP [1] but not the MEG. These phenomena are key to understanding the functional role of network frequencies and for investigating the stability of Gamma oscillations at both local and macroscopic levels. However, even at the most basic level of spatially- undifferentiated neuronal models, it is not fully understood how excitatory (E) and inhibitory (I) neurons interact to generate the observed network Gamma oscillations in the macaque single-unit and LFP data. For example we could obtain the frequency shift and power decay in a network where the Rhythm is produced by excitatory neurons that fired more frequently than inhibitory neurons, and in another more neurophysiologically plausible network composed of excitatory neurons showing sparse firing [2,3] and inhibitory neurons showing faster firing [4]. Moreover, it is unknown how increasing excitatory afferent drive (of which luminance contrast is a proxy) modulates the interactions between E and I populations (as well as interactions within each population) to account for changes in frequency and power. We aimed to replicate the empirical data from macaque visual cortex and to further investigate the stability of the observed Gamma oscillation. Here, we present an undifferentiated V1 network PING model, with realistic neuronal features as determined and validated from the analysis of a large number of V1 neurons obtained in 3 rhesus monkeys. The model when perturbed by increasing afferent input, exhibits the core characteristics of the empirical data, that is, (1) a monotonic increase in LFP frequency, (2) a non-monotonic LFP power modulation with decay at high inputs, (3) a largely non-saturating increase in average unit firing rate. In addition, the model exhibits realistic single unit behavior across a range of inputs. In terms of the frequency shift, we have observed remarkable scaling behaviour: while the frequency of oscillations changes dramatically with input, the absolute average phase at which inhibitory and excitatory neurons fire in each oscillation cycle and the average relative phase to each other remain constant. This scaling may on one hand underlie the stability of the Gamma oscillation locally and on other hand facilitate communication through coherence in the Gamma range [5] across varying stimulus conditions, by preserving the timing and relative ordering of population firing irrespective of the oscillation frequency [6]. Our results suggest that the observed power decline results from a primary (functional) decoupling among inhibitory neurons. Further analysis highlighted that the functional decoupling is related to the balance of inhibition/excitation. In further steps, we intend to test these predictions in the empirical data, and then proceed to a differentiated V1 columnar model to investigate the divergences between human MEG and macaque LFP/spiking responses.
